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  • Collinearity because of technical problem in Fixed Effects Panel Regression

    Hi,

    I am trying to look at the differences in income between African-Americans and White Americans. I am using panel data, and have run a fixed effects regression, which is the following:

    Code:
    xtreg linc i.highest_degree#bw i.bw#c.lhrswrk i.occup#bw ib2.urban_rural#bw i.marriage#bw i.bw#c.lwkswk_job i.census_region#bw, fe baselevel
    However, I am also running into a problem with collinearity, although it is strange in that it seems almost systematic:
    At the end of every interaction with a categorical variable, I have one value which is always omitted; that too always the very last category in the categorical variables. It doesn't happen for the continuous ones.

    Code:
    note: 1.educ#1.bw omitted because of collinearity
    note: 10.occup#1.bw omitted because of collinearity
    note: 2b.urban_rural#1.bw omitted because of collinearity
    note: 5.marriage#1.bw omitted because of collinearity
    note: 4.census_region#1.bw omitted because of collinearity
    Code:
             marriage#bw |
    Never married#White  |          0  (base)
    Never married#Black  |  -.5309271   .0851924    -2.02   0.043    -.3394337   -.0054707
          Married#White  |   .8552037   .0204159     7.87   0.000     .1206852    .2007177
          Married#Black  |   .4691787   .0831313     0.69   0.489    -.1054266    .2204569
    Separ, Divorc#White  |   .0947397   .0391489     4.68   0.000     .1065997    .2600673
    Separ, Divorc#Black  |          0  (omitted)
    
                         |
         bw#c.lwkswk_job |
                  White  |   .1013588    .004665    21.73   0.000     .0922152    .1105023
                  Black  |   .1059799   .0079389    13.35   0.000     .0904193    .1215405
    Is this a so-called 'technical problem', a problem with my formulation, or is it actually due to collinearity that STATA is finding in the data? Thank you very much.

  • #2
    Salman:
    1) some omitted variable might be due to the -fe- estimator behaviour (ie, wiping out every regressors that is time-invariant);
    2) other collinearity issues among the remaining predictors can exist.
    Eventually, I would not sponsor the way you build up interactions, as you seemingly kicked out from the set of predictors the so called conditional main effect of the predictors included in interactions.
    Kind regards,
    Carlo
    (Stata 19.0)

    Comment


    • #3
      Hi Carlo,

      Thank you for your advice.

      Regards,
      Salman

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